Testing the accuracy of analytical estimates of spare capacity in protected-mesh networks
Bibliographic record
Abstract
Recently, two different investigators published analytical models to predict the spare capacity requirements of shared-mesh survivable networks. If accurate, such estimators could be used in network planning and technology-selection applications in network-operating companies, displacing or reducing the need for detailed design studies. However, relatively few test-case results involving irregular topology and demands were provided, and some possibly significant idealizations were involved. We have therefore conducted a further series of tests of the equations to more widely assess the general accuracy of the results and to be aware of the possible limitations to their use. We review and implement the equations in question and compare their predictions, along with two well-known simple estimators, to the properties of integer linear programming (ILP)-based network design solutions for three families of protected-mesh networks. In all, 1464 detailed network designs are used as 'truth' tests for the equations over a systematically varying range of network topologies and demand patterns. On this set of trials the new mathematical models were rarely within 10% accuracy and typically had up to 30% error. By dissecting some specific cases we gain insights as to why average-case mathematical models of such a network-dependent phenomenon are unlikely to be reliable. Insights into the effects of network nodal degree, demand variance, hop and distance topologies, and topology dependence are also given.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".